In this course, you will :
- Deploy a flask application using Docker.
- Create a simple user interface (UI) to integrate the ML model, Watson NLU, and Watson Visual Recognition.
- Go over the fundamentals of Kubernetes terminology.
- Use Kubernetes to deploy a scalable web application.
- Discuss the various feedback loops in an AI workflow.
- Discuss the application of unit testing in the context of model development.
- Assess the bias and performance of production machine learning models using IBM Watson OpenScale.
1. Feedback loops and Monitoring
- Feedback Loops and Unit Testing
- Feedback Loops and Unit Tests
- Performance Monitoring and Business Metrics
- Performance Drift
2. Hands on with Openscale and Kubernetes
- Operationalize Trusted AI with IBM Watson OpenScale
- Kubernetes Explained
- Kubernetes vs. Docker: It's Not an Either/Or Question
3. Capstone: Pulling it all together (Part 1)
- What is in the Capstone and Associated Review?
- Review of Course 1: Business Priorities and Data Ingestion
- Review of Course 2: Data Analysis and Hypothesis Testing
- Review of Course 3: Feature Engineering and Bias Detection
- Review of Course 4: Machine Learning, Visual Recognition, and NLP
- Review of Course 5: Enterprise Model Deployment
- About the Data
4. Capstone: Pulling it all together (Part 2)
- Capstone Assignment 2: Through the Eyes of Our Working Example
- Capstone Part 2: Getting Started (Hands-On)
- Capstone Part 3: Getting Started (Hands-On)
- Solution Files